Model comparison
Claude Haiku 4.5 Thinking vs Claude Opus 4.7 (Adaptive)
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Haiku 4.5 Thinking unranked (Not scored); Claude Opus 4.7 (Adaptive) #27 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Claude Haiku 4.5 Thinking and Claude Opus 4.7 (Adaptive) share 1 comparable benchmark result. 0 of 8 categories are comparable. 1 result is unique to Claude Haiku 4.5 Thinking; 37 to Claude Opus 4.7 (Adaptive).
Updated July 23, 2026- Shared results
- 1
- Claude Haiku 4.5 Thinking only
- 1
- Claude Opus 4.7 (Adaptive) only
- 37
- Comparable categories
- 0 / 8
Benchmark data for Claude Haiku 4.5 Thinking and Claude Opus 4.7 (Adaptive) is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Claude Opus 4.7 (Adaptive) has the larger context window at 1M, compared with 200K for Claude Haiku 4.5 Thinking.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Claude Haiku 4.5 Thinking | Δ | Claude Opus 4.7 (Adaptive) |
|---|---|---|---|
| Agentic | Claude Haiku 4.5 ThinkingNot measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)75.1 |
| Coding | Claude Haiku 4.5 ThinkingNot measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)78.6 |
| Reasoning | Claude Haiku 4.5 ThinkingNot measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)75.8 |
| Knowledge | Claude Haiku 4.5 ThinkingNot measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)60.0 |
| Multimodal | Claude Haiku 4.5 ThinkingNot measured | MarginNo overlap | Claude Opus 4.7 (Adaptive)65.1 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Claude Haiku 4.5 Thinking | Claude Opus 4.7 (Adaptive) | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Haiku 4.5 ThinkingNot available | Claude Opus 4.7 (Adaptive)$5 input / $25 output | A complete price comparison is not available. |
| Generation speedtokens per second | Claude Haiku 4.5 ThinkingNot available | Claude Opus 4.7 (Adaptive)Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Haiku 4.5 ThinkingNot available | Claude Opus 4.7 (Adaptive)Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Haiku 4.5 Thinking200K | Claude Opus 4.7 (Adaptive)1M | Claude Opus 4.7 (Adaptive) lists the larger context window. |
Benchmark Deep Dive
Agentic12 benchmarks
| Benchmark | Claude Haiku 4.5 Thinking | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 69.4% | Not comparable |
| BrowseCompSource | — | 79.3% | Not comparable |
| MCP AtlasSource | — | 77.3% | Not comparable |
| OSWorld-VerifiedSource | — | 78% | Not comparable |
| CyberGymSource | — | 73.1% | Not comparable |
| AA Agentic IndexSource | — | 44.4% | Not comparable |
| τ²-bench resultsSource | — | 88.6% | Not comparable |
| GDPval-AASource | — | 49.8% | Not comparable |
| GDPval-AASource | — | 1495 | Not comparable |
| OSWorld 2.0Source | — | 18.2% | Not comparable |
| JobBenchSource | — | 45.9% | Not comparable |
| AA ITBenchSource | — | 46.7% | Not comparable |
Coding6 benchmarks
| Benchmark | Claude Haiku 4.5 Thinking | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| Vibe Code BenchSource | 11.39% | — | Not comparable |
| SWE-bench VerifiedSource | — | 87.6% | Not comparable |
| SWE-bench ProSource | — | 64.3% | Not comparable |
| Terminal-Bench 2.0Source | — | 69.4% | Not comparable |
| AA Coding IndexSource | — | 73.6% | Not comparable |
| AA-SciCodeSource | — | 54.5% | Not comparable |
Reasoning4 benchmarks
Knowledge10 benchmarks
| Benchmark | Claude Haiku 4.5 Thinking | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| GPQASource | — | 94.2% | Not comparable |
| GPQA-DSource | — | 94.2% | Not comparable |
| HLESource | — | 54.7% | Not comparable |
| HLE w/o toolsSource | — | 46.9% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 53.5% | Not comparable |
| AA-GPQA DiamondSource | — | 91.4% | Not comparable |
| AA-HLESource | — | 39.6% | Not comparable |
| AA-Omniscience IndexSource | — | 26.2% | Not comparable |
| AA-Omniscience AccuracySource | — | 45.8% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 36.2% | Not comparable |
Math1 benchmarks
| Benchmark | Claude Haiku 4.5 Thinking | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| FrontierMath (legacy)Source | — | 43.8% | Not comparable |
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | Claude Haiku 4.5 Thinking | Claude Opus 4.7 (Adaptive) | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 58.6% | Not comparable |
Frequently Asked Questions (3)
Can I compare Claude Haiku 4.5 Thinking and Claude Opus 4.7 (Adaptive) on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Claude Haiku 4.5 Thinking and Claude Opus 4.7 (Adaptive) today?
Claude Opus 4.7 (Adaptive): $5.00 input / $25.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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